Characterizing activity in a recurrent artificial neural network

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for characterizing activity in a recurrent artificial neural network. In one aspect, a method for identifying decision moments in a recurrent artificial neural network includes determining a complexity of patterns of activity in the recurrent artificial neural network, wherein the activity is responsive to input into the recurrent artificial neural network, determining a timing of activity having a complexity that is distinguishable from other activity that is responsive to the input, and identifying the decision moment based on the timing of the activity that has the distinguishable complexity.

BACKGROUND

This specification relates to the characterization of activity in arecurrent artificial neural network. The characterization of activitycan be used, e.g., in the identification of decision moments, as well asin encoding/decoding signals in contexts such as transmission,encryption, and data storage.

Artificial neural networks are devices that are inspired by thestructure and functional aspects of networks of biological neurons. Inparticular, artificial neural networks mimic the information encodingand other processing capabilities of networks of biological neuronsusing a system of interconnected constructs called nodes. Thearrangement and strength of connections between nodes in an artificialneural network determines the results of information processing orinformation storage by the artificial neural network.

Neural networks can be trained to produce a desired signal flow withinthe network and achieve desired information processing or informationstorage results. In general, training a neural network will change thearrangement and/or strength of connections between nodes during alearning phase. A neural network can be considered trained whensufficiently appropriate processing results are achieved by the neuralnetwork for given sets of inputs.

Artificial neural networks can be used in a variety of different devicesto perform non-linear data processing and analysis. Non-linear dataprocessing does not satisfy the superposition principle, i.e., thevariables that are to be determined cannot be written as a linear sum ofindependent components. Examples of contexts in which non-linear dataprocessing is useful include pattern and sequence recognition, speechprocessing, novelty detection and sequential decision making, complexsystem modelling, and systems and techniques in a variety of othercontexts.

SUMMARY

This specification describes technologies relating to thecharacterization of activity in an artificial neural network.

For example, a method for identifying decision moments in a neuralnetwork includes determining a complexity of patterns of activity in therecurrent artificial neural network, wherein the activity is responsiveto input into the recurrent artificial neural network, determining atiming of activity having a complexity that is distinguishable fromother activity that is responsive to the input, and identifying thedecision moments based on the timing of the activity that has thedistinguishable complexity.

As another example, a method for characterizing activity in a recurrentartificial neural network includes identifying pre-defined cliquepatterns of activity of the recurrent artificial neural network. Themethod is performed by data processing apparatus. As another example, amethod can include outputting a binary sequence of zeros and ones from arecurrent artificial neural network, wherein each digit in the sequencerepresents whether or not a particular group of nodes in the recurrentartificial neural network displays a respective pattern of activity.

As another example, a method of structuring a recurrent artificialneural network can include characterizing a complexity of patterns ofactivity that can arise in the recurrent artificial neural network thatcomprises a structured collection of nodes and links between the nodes,and evolving the structure of the recurrent artificial neural network toincrease the complexity of the patterns of activity. This method ofstructuring can also be used, e.g., a part of a method of training therecurrent artificial neural network.

Other embodiments of these aspects include corresponding systems,apparatus, and computer programs, configured to perform the actions ofthe methods, encoded on computer storage devices.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. For example, traditional data processing devicessuch as, e.g., digital and other computers are programmed to follow apre-defined logic sequence when processing information. The moment whena computer arrives at a result is thus relatively easy to identify.Namely, the completion of the logic sequence embodied in the programmingindicates when information processing is complete and the computer has“reached a decision.” The result can be held at the output of thecomputer's data processor in a relatively long-lived form by, e.g., amemory device, a set of buffers, or the like, and accessed for a varietyof purposes.

In contrast, as described herein, decision moments in artificialrecurrent neural networks can be identified based on characteristics ofthe dynamic properties of the neural network during informationprocessing. Rather than waiting for the artificial neural network toreach a pre-defined end of a logic sequence, decision moments inartificial neural networks can be identified based on characteristics ofthe functional states of the artificial neural network in the midst ofinformation processing.

Further, the characteristics of the dynamic properties of recurrentartificial neural networks during information processing—includingcharacteristics such as the activity comporting with clique patterns anddirected clique patterns—can be used in a variety of signallingoperations, including signal transmission, encoding, encryption, andstorage. In particular, the characteristics of the activity in arecurrent artificial neural network during information processingreflects the input and can be considered to be an encoded form of theinput (i.e., the “output” of a recurrent artificial neural network inencoding processes). These characteristics can be, e.g., transmitted toa remote receiver which could decode the transmitted characteristics toreconstitute the input or a portion of the input.

Moreover, in some cases, activity in different groups of nodes arecurrent artificial neural network (e.g., activity that comports withclique patterns and directed clique patterns) can be represented as abinary sequence of zeros and ones, each digit indicating whether or notthat activity comports with a pattern. Since the activity can be theoutput of a recurrent artificial neural network in some contexts, theoutput of a recurrent artificial neural network can be represented as avector of binary digits and compatible with digital data processing.

Further, in some cases, such characterization of the dynamic propertiesof a recurrent artificial neural network can be used prior to and/orduring training to increase the likelihood of complex patterns ofactivity arising during information processing. For example, before orduring training, the links between nodes in a recurrent neural networkcan be intentionally evolved to increase the complexity of activitypatterns. For example, the links between nodes in a recurrent artificialneural network can be intentionally evolved to increase the likelihoodthat, e.g., clique patterns and directed clique patterns of activityarise during information processing. This can reduce the time and effortneeded to train a recurrent artificial neural network.

As another example, such characterization of the dynamic properties of arecurrent artificial neural network can be used to determine a degree ofcompleteness in the training of a recurrent neural network. For example,a recurrent artificial neural network that displays particular types ofordering in the activity (e. g, clique patterns and directed cliquepatterns) can be considered more highly trained than a recurrentartificial neural network that does not display such ordering. Indeed,in some cases, the extent of training can be quantified by quantifyingthe degree of ordering of the activity in the recurrent artificialneural network.

The details of one or more implementations described in thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of the structure of recurrentartificial neural network device.

FIGS. 2 and 3 are schematic illustrations of the function of recurrentartificial neural network device in different windows of time.

FIG. 4 is a flowchart of a process for identifying decision moments in arecurrent artificial neural network based on characterization of theactivity in the network.

FIG. 5 is a schematic illustration of patterns of activity that can beidentified and used for identifying decision moments in a recurrentartificial neural network.

FIG. 6 is a schematic illustration of patterns of activity that can beidentified and used for identifying decision moments in a recurrentartificial neural network.

FIG. 7 is a schematic illustration of patterns of activity that can beidentified and used for identifying decision moments in a recurrentartificial neural network.

FIG. 8 is a schematic illustration of a data table that can be used in adetermination of the complexity or degree of ordering in the activitypatterns in a recurrent artificial neural network device.

FIG. 9 is a schematic illustration of a determination of the timing ofactivity patterns that have a distinguishable complexity.

FIG. 10 is a flowchart of a process for encoding signals using arecurrent artificial neural network based on characterization of theactivity in the network.

FIG. 11 is a flowchart of a process for decoding signals using arecurrent artificial neural network based on characterization of theactivity in the network.

FIGS. 12, 13, and 14 are schematic illustrations of an identical binaryform or representation of topological structures.

FIGS. 15 and 16 schematically illustrate an example of how the presenceor absence of features that correspond to different bits are notindependent of one another.

FIGS. 17, 18, 19, 20 are schematic illustrations of the use ofrepresentations of the occurrence of topological structures in theactivity in a neural network in four different classification systems.

FIGS. 21, 22 are schematic illustration of edge devices that include alocal artificial neural network that can be trained usingrepresentations of the occurrence of topological structurescorresponding to activity in a source neural network.

FIG. 23 is a schematic representation of a system in which local neuralnetworks can be trained using representations of the occurrence oftopological structures corresponding to activity in a source neuralnetwork.

FIGS. 24, 25, 26, 27 are schematic illustrations of the use ofrepresentations of the occurrence of topological structures in theactivity in a neural network in four different systems.

FIG. 28 is a schematic illustration of a system 0 that includes anartificial neural network that can be trained using representations ofthe occurrence of topological structures corresponding to activity in asource neural network.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIG. 1 is a schematic illustration of the structure of a recurrentartificial neural network device 100. Recurrent artificial neuralnetwork device 100 is a device that mimics the information encoding andother processing capabilities of networks of biological neurons using asystem of interconnected nodes. Recurrent artificial neural networkdevice 100 can be implemented in hardware, in software, or incombinations thereof.

The illustration of recurrent artificial neural network device 100includes a plurality of nodes 101, 102, . . . , 107 that areinterconnected by a plurality of structural links 110. Nodes 101, 102, .. . , 107 are discrete information processing constructs that areanalogous to neurons in biological networks. Nodes 101, 102, . . . , 107generally process one or more input signals received over one or more oflinks 110 to produce one or more output signals that are output over oneor more of links 110. For example, in some implementations, nodes 101,102, . . . , 107 can be artificial neurons that weight and sum multipleinput signals, pass the sum through one or more non-linear activationfunctions, and output one or more output signals.

Nodes 101, 102, . . . , 107 can operate as accumulators. For example,nodes 101, 102, . . . , 107 can operate in accordance with anintegrate-and-fire model in which one or more signals accumulate in afirst node until a threshold is reached. After the threshold is reached,the first node fires by transmitting an output signal to a connected,second node along one or more of links 110. In turn, the second node101, 102, . . . , 107 accumulates the received signal and, if athreshold is reached, then the second node 101, 102, . . . , 107transmits yet another output signal to a further connected node.

Structural links 110 are connections that are capable of transmittingsignals between nodes 101, 102, . . . , 107. For the sake ofconvenience, all structural links 110 are treated herein as identicalbidirectional links that convey a signal from every first of nodes 101,102, . . . , 107 to every second of nodes 101, 102, . . . , 107 inidentically the same manner as a signal is conveyed from the second tothe first. However, this is not necessarily the case. For example, someportion or all of structural links 110 can be unidirectional links thatconvey a signal from a first of nodes 101, 102, . . . , 107 to a secondof nodes 101, 102, . . . 107 without conveying signals from the secondto the first.

As another example, in some implementations, structural links 110 canhave diverse properties other than or in addition to directionality. Forexample, in some implementations, different structural links 110 cancarry signals of different magnitudes—resulting in a different strengthsof interconnection between respective of nodes 101, 102, . . . , 107. Asanother example, different structural links 110 can carry differenttypes of signal (e.g., inhibitory and/or excitatory signals). Indeed, insome implementations, structural links 110 can be modelled on the linksbetween soma in biological systems and reflect at least a portion of theenormous morphological, chemical, and other diversity of such links.

In the illustrated implementation, recurrent artificial neural networkdevice 100 is a clique network (or subnetwork) in that every node 101,102, . . . , 107 is connected to every other node 101, 102, . . . , 107.This is not necessarily the case. Rather, in some implementations, eachnode 101, 102, . . . , 107 can be connected to a proper subset of nodes101, 102, . . . , 107 (by identical links or diverse links, as the casemay be).

For the sake of clarity of illustration, recurrent artificial neuralnetwork device 100 is illustrated with only seven nodes. In general,real-world neural network devices will include significantly largernumbers of nodes. For example, in some implementations, neural networkdevices can include hundreds of thousands, millions, or even billions ofnodes. Thus, recurrent neural network device 100 can be a fraction of alarger recurrent artificial neural network (i.e., a subnetwork).

In biological neural network devices, accumulation and signaltransmission processes require the passage of time in the real world.For example, the soma of a neuron integrates input received over time,and signal transmission from neuron to neuron requires times that aredetermined by, e.g., the signal transmission velocity and the nature andlength of the links between neurons. Thus, the state of a biologicalneural network device is dynamic and changes over time.

In artificial recurrent neural network devices, time is artificial andrepresented using mathematical constructs. For example, rather thanrequiring a real world passage of time for signals to transmit from nodeto node, such signals can be represented in terms of artificial unitsthat are generally unrelated to the real world passage of time—asmeasured in computer clock cycles or otherwise. Nevertheless, the stateof an artificial recurrent neural network device can be described as“dynamic” in that it changes with respect to these artificial units.

Please note that, for the sake of convenience, these artificial unitsare referred to herein as “time” units. Nevertheless, it is to beunderstood that these units are artificial and generally do notcorrespond to the real world passage of time.

FIGS. 2 and 3 are schematic illustrations of the function of recurrentartificial neural network device 100 in different windows of time.Because the state of device 100 is dynamic, the functioning of device100 can be represented using the signal transmission activity thatoccurs within a window. Such a functional illustration generally showsactivity in only a fraction of links 110. In particular, since ingeneral not every link 110 conveys a signal within a particular window,not every link 110 is illustrated as actively contributing to thefunctioning of the device 100 in these illustrations.

In the illustrations of FIGS. 2 and 3, an active link 110 is illustratedas a relatively thick solid line connecting a pair of nodes 101, 102, .. . , 107. In contrast, inactive links 110 are illustrated as dashedlines. This is for the sake of illustration only. In other words, thestructural connections formed by links 110 exist whether or not links110 are active. However, this formalism highlights activity and thefunctioning of device 100.

In addition to schematically illustrating the existence of activityalong a link, the direction of the activity is also schematicallyillustrated. In particular, the relatively thick solid lines thatillustrate active of links 110 also include arrow heads that denote thedirection of signal transmission along the link during the relevantwindow. In general, the direction of signal transmission in a singlewindow does not conclusively constrain the link to being aunidirectional link having the indicated directionality. Rather, in afirst functional illustration for a first window of time, a link can beactive in a first direction. In a second functional illustration for asecond window, a link can be active in the opposite direction. However,in some cases such as, e.g., in a recurrent artificial neural networkdevice 100 that exclusively includes unidirectional links, thedirectionality of signal transmission will conclusively indicate thedirectionality of the link.

In feedforward neural network devices, information moves exclusively ina single direction (i.e., forward) to an output layer of nodes that isat the end of the network. Feedforward neural network devices indicatethat a “decision” has been reached and that information processing iscomplete by the propagation of the signals through the network to theoutput layer.

In contrast, in recurrent neural networks, the connections between nodesform cycles and the activity of the network dynamically progresseswithout a readily identifiable decision. For example, even in athree-node recurrent neural network, the first node can transmit asignal to the second node, which in response can transmit a signal tothe third. In response, the third node can transmit a signal back to thefirst. The signals received by the first node can be responsive—at leastin part—to the signals transmitted from that same node.

The schematic functional illustrations FIGS. 2 and 3 illustrate this ina network that is only slightly larger than a three-node recurrentneural network. The functional illustrations shown in FIG. 2 can beillustrative of activity within a first window and FIG. 3 can beillustrative of activity within a second, immediately following. Asshown, a collection of signal transmission activity appears to originatein node 104 and progress in a generally clockwise direction throughdevice 100 during the first window. In the second window, at least someof the signal transmission activity generally appears to return to node104. Even in such a simplistic illustration, signal transmission doesnot proceed in a manner that yields a clearly identifiable output orend.

When a recurrent neural network of, e.g., thousands of nodes or more isconsidered, it can be recognized that signal propagation can occur overa huge number of paths and that these signals lack a clearlyidentifiable “output” location or time. Although the network may bydesign return to a quiescent state in which only background or even nosignal transmission activity occurs, the quiescent state itself does notindicate the results of information processing. The recurrent neuralnetwork always returns to the quiescent state regardless of the input.The “output” or the result of the information processing is thus encodedin the activity that occurs within the recurrent neural network inresponse to a particular input.

FIG. 4 is a flowchart of a process 400 for identifying decision momentsin a recurrent artificial neural network based on characterization ofthe activity in the network. A decision moment is a point of time whenthe activity in a recurrent artificial neural network is indicative ofthe results of information processing by the network in response to aninput. Process 400 can be performed by a system of one or more dataprocessing apparatus that perform operations in accordance with thelogic of one or more sets of machine-readable instructions. For example,process 400 can be performed by that same system of one or morecomputers that executes software for implementing the recurrentartificial neural network used in process 400.

The system performing process 400 receives a notification that a signalhas been input into the recurrent artificial neural network at 405. Insome cases, the input of the signal is a discrete injection event inwhich, e.g., information is injected into one or more nodes and/or oneor more links of the neural network. In other cases, the input of thesignal is a stream of information that is injected into the one or morenodes and/or links of the neural network over a period of time. Thenotification indicates that the artificial neural network is activelyprocessing information and not, e.g., in a quiescent state. In somecases, the notification is received from the neural network itself,e.g., such as when the neural network exits an identifiable quiescentstate.

The system performing process 400 divides the responsive activity in thenetwork into a collection of windows at 410. In cases where injection isa discrete event, the windows can subdivide the time between injectionand a return to a quiescent state into a number of periods during whichthe activity displays variable complexities. In cases where injection isa stream of information, the duration of the injection (and optionallythe time to return to a quiescent state after injection is complete) canbe subdivided into windows during which the activity displays variablecomplexities. Various approaches to determining the complexity ofactivity are discussed further below.

In some implementations, the windows all have the same duration, butthis is not necessarily the case. Rather, in some implementations, thewindows can have different durations. For example, in someimplementations, duration can increase as time since a discreteinjection event has occurred increases.

In some implementations, the windows can be a successive series ofseparate windows. In other implementations, the windows overlap in timeso that one window begins before a previous window ends. In some cases,the windows can be a moving window that moves in time.

In some implementations, different durations of windows are defined fordifferent determinations of the complexity of activity. For example, foractivity patterns that define activity occurring between relativelylarger numbers of nodes, the windows can have a relatively longerduration than windows that are defined for activity patterns that defineactivity occurring between relatively smaller numbers of nodes. Forexample, in the context of patterns 500 of activity (FIG. 5), a windowthat is defined for identifying activity that comports with pattern 530can be longer than a window that is defined for identifying activitythat comports with pattern 505.

The system performing process 400 identifies patterns in the activity inthe network in the different windows at 415. As discussed further below,patterns in the activity can be identified by treating a functionalgraph as a topological space with nodes as points. In someimplementation, the activity patterns that are identified are cliques,e.g., directed cliques, in a functional graph of the network.

The system performing process 400 determines the complexity of theactivity patterns in different windows at 420. The complexity can be ameasure of the likelihood that an ordered pattern of activity ariseswithin a window. Thus, activity patterns that arise randomly would berelatively simple. On the other hand, activity patterns that shownon-random order are relatively complex. For example, in someimplementations, the complexity of an activity pattern can be measuredusing, e.g., the simplex counts or the Betti numbers of the activitypattern.

The system performing process 400 determines the timing of activitypatterns having a distinguishable complexity at 425. A particularactivity pattern can be distinguishable based on a complexity thatdeviates upwards or deviates downward, e.g., from a fixed or a variablebaseline. In other words, the timing of activity patterns that indicateparticularly high levels or particularly low levels of non-random orderin the activity can be determined.

For example, in cases where signal input is a discrete injection event,deviations, e.g., from a stable baseline or from a curve that ischaracteristic of the neural network's average response to a variety ofdifferent discrete injection events can be used to determine the timingof distinguishable complex activity patterns. As another example, incases where information is input in a stream, large changes incomplexity during streaming can be used to determine the timing ofdistinguishable complex activity patterns.

The system performing process 400 times the reading of the output fromthe neural network based on the timing of distinguishably complexactivity patterns at 430. For example, in some implementations, theoutput of the neural network can be read at the same time thatdistinguishable complex activity patterns arise. In implementationswhere the complexity deviations indicate a relatively high non-randomorder in the activity, the observed activity patterns themselves canalso be taken as the output of the recurrent artificial neural network.

FIG. 5 is an illustration of patterns 500 of activity that can beidentified and used for identifying decision moments in a recurrentartificial neural network. For example, patterns 500 can be identifiedat 415 in process 400 (FIG. 4).

Patterns 500 are illustrations of activity within a recurrent artificialneural network. During the application of patterns 500, a functionalgraph is treated as a topological space with nodes as points. Activityin nodes and links that comports with patterns 500 can be recognized asordered regardless of the identity of the particular nodes and/or linksthat participate in the activity. For example, first pattern 505 canrepresent the activity between nodes 101, 104, 105 in FIG. 2, with point0 in pattern 505 as node 104, point 1 as node 105, and point 2 as node101. As another example, first pattern 505 can also represent theactivity between nodes 104, 105, 106 in FIG. 3, with point 0 in pattern505 as node 106, point 1 as node 104, and point 2 as node 105. The orderof activity in the directed cliques is also specified. For example, inpattern 505, activity between point 1 and point 2 occurs after theactivity between point 0 and point 1.

In the illustrated implementation, patterns 500 are all directed cliquesor directed simplices. In such patterns, activity originates from asource node that transmits signals to every other node in the pattern.In patterns 500, such source nodes are designated as point 0 whereas theother nodes are designated as points 1, 2, . . . . Further, in directedcliques or simplices, one of the nodes acts a sink and receives signalstransmitted from every other node in the pattern. In patterns 500, suchsink nodes are designated as the highest numbered point in the pattern.For example, in pattern 505, the sink node is designated as point 2. Inpattern 510, the sink node is designated as point 3. In pattern 515, thesink node is designated as point 3, and so on. The activity representedby patterns 500 is thus ordered in a distinguishable manner.

Each of patterns 500 has a different number of points and reflectsordered activity in a different number of nodes. For example, pattern505 is a 2D-simplex and reflects activity in three nodes, pattern 510 isa 3D-simplex and reflects activity in four nodes, and so on. As thenumber of points in a pattern increases, so does the degree of orderingand the complexity of the activity. For example, for a large collectionof nodes that have a certain level of random activity within a window,some of that activity may comport with pattern 505 out of happenstance.However, it is progressively more unlikely that random activity willcomport with the respective of patterns 510, 515, 520 . . . . Thepresence of activity that comports with pattern 530 indicates arelatively higher degree of ordering and complexity in the activity thatthe presence of activity that comports with pattern 505.

As discussed previously, in some implementations, different durationwindows can be defined for different determinations of the complexity ofactivity. For example, when activity that comports with pattern 530 isto be identified, longer duration windows can be used than when activitythat comports with pattern 505 is to be identified.

FIG. 6 is an illustration of patterns 600 of activity that can beidentified and used for identifying decision moments in a recurrentartificial neural network. For example, patterns 600 can be identifiedat 415 in process 400 (FIG. 4).

Like patterns 500, patterns 600 are illustrations of activity within arecurrent artificial neural network. However, patterns 600 depart fromthe strict ordering of patterns 500 in that patterns 600 are not alldirected cliques or directed simplices. In particular, patterns 605, 610have a lower directionality than pattern 515. Indeed, pattern 605 lacksa sink node altogether. Nevertheless, patterns 605, 610 indicate degreeof ordered activity that exceeds that expected through randomhappenstance and can be used to determine the complexity of activity ina recurrent artificial neural network.

FIG. 7 is an illustration of patterns 700 of activity that can beidentified and used for identifying decision moments in a recurrentartificial neural network. For example, patterns 700 can be identifiedat 415 in process 400 (FIG. 4).

Patterns 700 are groups of directed cliques or directed simplices of thesame dimension (i.e., have the same number of points) that definepatterns involving more points than the individual cliques or simplicesand enclose cavities within the group of directed simplices.

By way of example, pattern 705 includes six different three point,2-dimensions patterns 505 that together define a homology class ofdegree two, whereas pattern 710 includes eight different three point,2-dimensions patterns 505 that together define a second homology classof degree two. Each of the three point, 2-dimensions patterns 505 inpatterns 705, 710 can be thought of as enclosing a respective cavity.The n^(th) Betti number associated with a directed graph provides acount of such homology classes within a topological representation.

The activity illustrated by patterns such as patterns 700 illustrates arelatively high degree of ordering of the activity within a network thatis unlikely to arise by random happenstance. Patterns 700 can be used tocharacterize the complexity of that activity.

In some implementations, only some patterns of activity are identifiedand/or some portion of the patterns of activity that are identified arediscarded or otherwise ignored during the identification of decisionmoments. For example, with reference to FIG. 5, activity that comportswith the five point, 4-dimensional simplex pattern 515 inherentlyincludes activity that comports with the four point, 3-dimensional andthree point, 2-dimension simplex patterns 510, 505. For example, points0, 2, 3, 4 and points 1, 2, 3, 4 in 4-dimensional simplex pattern 515 ofFIG. 5 both comport with 3-dimensional simplex pattern 510. In someimplementations, patterns that include fewer points—and hence are of alower dimension—can be discarded or otherwise ignored during theidentification of decision moments.

As another example, only some patterns of activity need be identified.For example, in some implementations only patterns with odd number ofpoints (3, 5, 7, . . . ) or even numbers of dimensions (2, 4, 6, . . . )are used in the identification of decision moments.

The complexity or degree of ordering in the activity patterns in arecurrent artificial neural network device in different windows can bedetermined in a variety of different ways. FIG. 8 is a schematicillustration of a data table 800 that can be used in such adetermination. Data table 800 can be used to determine the complexity ofthe activity patterns in isolation or in conjunction with otheractivities. For example, data table 800 can be used at 420 in process400 (FIG. 4).

In further detail, table 800 includes a number count of patternoccurrences during a window “N,” where the number counts of activitythat matches patterns of different dimensions are presented in differentrows. For example, in the illustrated example, row 805 includes a numbercount (i.e., “2032”) of the occurrences of activity that matches one ormore three point, 2-dimensional patterns, whereas row 810 includes anumber count (i.e., “877”) of the occurrences of activity that matchesone or more four point, 3-dimensional patterns. Since an occurrence ofthe patterns indicates that the activity has an order which isnon-random, the number counts also provide generalized characterizationof the overall complexity of the activity patterns. A table that isanalogous to table 800 can be formed for each window that is defined,e.g., at 410 in process 400 (FIG. 4).

Although table 800 includes a separate row and a separate entry forevery type of activity pattern, this is not necessarily the case. Forexample, one of more counts can be omitted (e.g., counts of simplerpatterns) can be omitted from table 800 and from a determination of thecomplexity. As another example, some implementations, a single row orentry can include counts of occurrences of multiple activity patterns.

Although FIG. 8 presents the number count in a table 800, this is notnecessarily the case. For example, the number count can be presented asa vector (e.g., <2032, 877, 133, 66, 48, . . . >). Regardless of how thecount is presented, in some implementations, the counts can be expressedin binary and can be compatible with digital data processinginfrastructure.

In some implementations, number counts of the occurrences of thepatterns can be weighted or combined to determine the degree orcomplexity of the ordering, e.g., at 420 in process 400 (FIG. 4). Forexample, the Euler characteristic can provide an approximation of thecomplexity of the activity and is given by:

S₀−S₁+S₂−S₃  EQUATION 1

where S_(n) is number of occurrences of a pattern of n points (i.e., apattern of dimensionality n−1). The patterns can be, e.g., the directedclique patterns 500 (FIG. 5).

As another example of how number counts of the occurrences of thepatterns can be weighted to determine the degree or complexity of theordering, in some implementations pattern occurrences can be weightedbased on the weights of the links that are active. In further detail, asdiscussed previously, the strength of connection between nodes in anartificial neural network can vary, e.g., as a consequence of how activethe connection was during training. An occurrence of a pattern ofactivity along a collection of relatively stronger links can be weighteddifferently from the occurrence of that same pattern of activity along acollection of relatively weaker links. For example, in someimplementations, the sum of the weight of the links that are active canbe used to weight the occurrence.

In some implementations, the Euler characteristic or other measure ofcomplexity can be normalized by the total number of patterns that arematched within a particular window and/or the total number of patternsthat it is possible for a given network to form given its structure. Anexample of a normalization with regard to the total number of patternsthat it is possible for a network to form is given below in Equations 2,3.

In some implementations, occurrences of higher dimension patternsinvolving larger numbers of nodes can be weighted more heavily thanoccurrences of lower dimension patterns involving smaller numbers ofnodes. For example, the probability of forming directed cliquesdecreases rapidly with increasing dimension. In particular, to form ann-clique from n+1 nodes, one needs (n+1)n/2 edges all orientedcorrectly. This probability can be reflected in the weighting.

In some implementations, both the dimension and the directionality ofpatterns can be used to weight occurrences of patterns and determine thecomplexity of the activity. For example, with reference to FIG. 6,occurrences of five point, 4-dimensional pattern 515 can be weightedmore heavily than occurrences of five point, 4-dimensional patterns 605,610 in accordance with the differences in directionality of thosepatterns.

An example of the use of both the directionality and the dimension ofpatterns to determine the degree of ordering or complexity of theactivity can be given by

$\begin{matrix}{\frac{\begin{matrix}{{S_{0}^{active}\left( n_{0} \right)}^{3} - {S_{1}^{active}\left( n_{1} \right)}^{3} +} \\{ {{S_{2}^{active}\left( n_{2} \right)}^{3} - {{S_{3}^{active}\left( n_{3} \right)}^{3} \ldots}}}\end{matrix}}{ERN}/{SC}} & {{EQUATION}\mspace{14mu} 2}\end{matrix}$

where S_(x) ^(active) indicates the number of active occurrences of apattern of n points and ERN is the calculation for an equivalent randomnetwork, i.e., a network with the same number of nodes randomlyconnected. Further, SC is given by

$\begin{matrix}{{SC} = \frac{{S_{0}^{silent}\left( n_{0} \right)}^{3} - {S_{1}^{silent}\left( n_{1} \right)}^{3} + {S_{2}^{silent}\left( n_{2} \right)}^{3} - {{S_{3}^{silent}\left( n_{3} \right)}^{3} \ldots}}{({ERN})}} & {{EQUATION}\mspace{14mu} 3}\end{matrix}$

where S_(x) ^(silent) indicates the number of occurrences of a patternof n points when the recurrent artificial neural network is silent andcan be thought of as embodying the total number of patterns that it ispossible for the network to form. In Equations 2, 3, the patterns canbe, e.g., the directed clique patterns 500 (FIG. 5).

FIG. 9 is a schematic illustration of a determination of the timing ofactivity patterns that have a distinguishable complexity. Thedetermination illustrated in FIG. 9 can be performed in isolation or inconjunction with other activities. For example, the determination can beperformed at 425 in process 400 (FIG. 4).

FIG. 9 includes a graph 905 and a graph 910. Graph 905 illustratesoccurrences of patterns as a function of time along the x-axis. Inparticular, individual occurrences are illustrated schematically asvertical lines 906, 907, 908, 909. Each row of occurrences can beinstances where activity matches a respective pattern or class ofpattern. For example, the top row of occurrences can be instances whereactivity matches pattern 505 (FIG. 5), the second row of occurrences canbe instances where activity matches pattern 510 (FIG. 5), the third rowof occurrences can be instances where activity matches pattern 515 (FIG.5), and so on.

Graph 905 also includes dashed rectangles 915, 920, 925 thatschematically delineate different windows of time when the activitypatterns have a distinguishable complexity. As shown, the likelihoodthat activity in the recurrent artificial neural network matches apattern indicative of complexity is higher during the windows delineatedby dashed rectangles 915, 920, 925 than outside those windows.

Graph 910 illustrates the complexity associated with these occurrencesas a function of time along the x-axis. Graph 910 includes a first peak930 in complexity that coincides with the window delineated by dashedrectangle 915 and a second peak 935 in complexity that coincides withthe window delineated by dashed rectangles 920, 925. As shown, thecomplexity illustrated by peaks 930, 925 is distinguishable from whatcan be considered to be a baseline level 940 of complexity.

In some implementations, the times at which the output of a recurrentartificial neural network is to be read coincide with the occurrences ofactivity patterns that have a distinguishable complexity. For example,in the illustrative context of FIG. 9, the output of a recurrentartificial neural network can be read at peaks 930, 925, i.e., duringthe windows delineated by dashed rectangles 915, 920, 925.

The identification of distinguishable levels of complexity in arecurrent artificial neural network is particularly beneficial when theinput is a stream of data. Examples of data streams include, e.g., videoor audio data. Although data streams have a beginning, it is generallydesirable to process information in the data stream that does not have apre-defined relationship with the beginning of the data stream. By wayof example, a neural network could perform object recognition such as,e.g., recognizing bicyclists in the vicinity of an automobile. Such aneural networks should be able to recognizing bicyclists regardless ofwhen those bicyclists appear in the video stream, i.e., without regardto time since the beginning of the video. Continuing with this example,when a data stream is input into an object recognition neural network,any patterns of activity in the neural network will generally display alow or quiescent level of complexity. These low or quiescent level ofcomplexity are displayed regardless of the continuous (or nearlycontinuous) input of streaming data into the neural network device.However, when an object of interest appears in the video stream, thecomplexity of the activity will become distinguishable and indicate thetime at which an object is recognized in the video stream. Thus, thetiming of a distinguishable level of complexity of the activity can alsoact as a yes/no output as to whether the data in the data streamsatisfies certain criteria.

In some implementations, not only the timing but also the content of theoutput of the recurrent artificial neural network is given by theactivity patterns that have a distinguishable complexity. In particular,the identity and activity of the nodes that participate in activity thatcomports with the activity patterns can be considered the output of therecurrent artificial neural network. The identified activity patternscan thus illustrate the result of processing by the neural network, aswell as the timing when this decision is to be read.

The content of the decision can be expressed in a variety of differentforms. For example, in some implementations and as discussed in furtherdetail below, the content of the decision can be expressed as a binaryvector or matrix of ones and zeros. Each digit can indicate whether ornot a pattern of activity is present or not, e.g., for a pre-definedgroup of nodes and/or a predefined duration. In such implementations,the content of the decision is expressed in binary and can be compatiblewith traditional digital data processing infrastructure.

FIG. 10 is a flowchart of a process 1000 for encoding signals using arecurrent artificial neural network based on characterization of theactivity in the network. Signals can be encoded in a variety ofdifferent contexts such as, e.g., transmission, encryption, and datastorage. Process 1000 can be performed by a system of one or more dataprocessing apparatus that perform operations in accordance with thelogic of one or more sets of machine-readable instructions. For example,process 1000 can be performed by that same system of one or morecomputers that executes software for implementing the recurrentartificial neural network used in process 1000. In some instances,process 1000 can be performed by the same data processing apparatus thatperforms process 400. In some instances, process 1000 can be performedby, e.g., the encoder in a signal transmission system or the encoder ofa data storage system.

The system performing process 1000 inputs a signal into a recurrentartificial neural network at 1005. In some cases, the input of thesignal is a discrete injection event. In others, the input signal isstreamed into the recurrent artificial neural network.

The system performing process 1000 identifies one or more decisionmoments in the recurrent artificial neural network at 1010. For example,the system can identify one or more decision moments by performingprocess 400 (FIG. 4).

The system performing process 1000 reads the output of the recurrentartificial neural network at 1015. As discussed above, in someimplementations, the content of the output of the recurrent artificialneural network is the activity in the neural network that matches thepatterns used to identify the decision point(s).

In some implementations, individual “reader nodes” can be added to aneural network to identify occurrences of a particular pattern ofactivity at a particular collection of nodes and hence to read theoutput of the recurrent artificial neural network at 1015. The readernodes can fire if and only if the activity at a particular collection ofnodes satisfies timing (and possibly magnitude, as well) criteria. Forexample, in order to read an occurrence of pattern 505 (FIG. 5) at nodes104, 105, 106 (FIGS. 2, 3), a reader node could be connected to nodes104, 105, 106 (or the links 110 between them). The reader node woulditself only become active if a pattern of activity involving nodes 104,105, 106 (or their links) occurred.

The use of such reader nodes would eliminate the need to define windowsof time for the recurrent artificial neural network as a whole. Inparticular, individual reader nodes can be connected to different nodesand/or numbers of nodes (or the links between them). The individualreader nodes can be set to have tailored responses (e.g., differentdecay times in an integrate-and-fire model) to identify differentactivity patterns. The system performing process 1000 transmits orstores the output of the recurrent artificial neural network at 1020.The particular action performed at 1020 can reflect the context in whichprocess 1000 is being used. For example, in contexts where secure orcompressed communications are desired, the system performing process1000 can transmit the output of the recurrent neural network to areceiver that has access to the same or to a similar recurrent neuralnetwork. As another example, in contexts where secure or compressed datastorage is desired, the system performing process 1000 can record theoutput of the recurrent neural network in one or more machine-readabledata storage devices for later access.

In some implementations, the complete output of the recurrent neuralnetwork is not transmitted or stored. For example, in implementationswhere the content of the output of the recurrent neural network is theactivity in the neural network that matches the patterns indicative ofcomplexity in the activity, only activity that matches relatively morecomplex or higher dimensional activity may be transmitted or stored. Byway of example, in reference to patterns 500 (FIG. 5), in someimplementations only activity that matches patterns 515, 520, 525, and530 is transmitted or stored whereas activity that matches patterns 505,510 is ignored or discarded. In this way, a lossy process allows thevolume of data that is transmitted or stored to be reduced at the costof the completeness of the information being encoded.

FIG. 11 is a flowchart of a process 1100 for decoding signals using arecurrent artificial neural network based on characterization of theactivity in the network. Signals can be decoded in a variety ofdifferent contexts such as, e.g., signal reception, decryption, andreading data from storage. Process 1100 can be performed by a system ofone or more data processing apparatus that perform operations inaccordance with the logic of one or more sets of machine-readableinstructions. For example, process 1100 can be performed by that samesystem of one or more computers that executes software for implementingthe recurrent artificial neural network used in process 1100. In someinstances, process 1100 can be performed by the same data processingapparatus that performs process 400 and/or process 1000. In someinstances, process 1100 can be performed by, e.g., the decoder in asignal reception system or the decoder of a data storage system.

The system performing process 1100 receives at least a portion of theoutput of a recurrent artificial neural network at 1105. The particularaction performed at 1105 can reflect the context in which process 1100is being used. For example, the system performing process 1000 canreceive a transmitted signal that includes the output of the recurrentartificial neural network or read a machine-readable data storage devicethat stores the output of the recurrent artificial neural network.

The system performing process 1100 reconstructs the input of therecurrent artificial neural network from the received output at 1110.Reconstruction can proceed in a variety of different ways. For example,in some implementations, a second artificial neural network (recurrentor not) can be trained to reconstruct the input into the recurrentneural network from the output received at 1105.

As another example, in some implementations, a decoder that has beentrained using machine learning (including but not limited to deeplearning) can be trained to reconstruct the input into the recurrentneural network from the output received at 1105.

As yet another example, in some implementations, input into the samerecurrent artificial neural network or into a similar recurrentartificial neural network can be iteratively permuted until the outputof that recurrent artificial neural network matches, to some degree, theoutput received at 1105.

In some implementations, process 1100 can include receiving user inputspecifying an extent to which the input is to be reconstructed and, inresponse, adjust the reconstruction at 1110 accordingly. For example,the user input could specify that a complete reconstruction is notneeded. In response, the system performing process 1100 adjusts thereconstruction. For example, in implementations where the content of theoutput of the recurrent neural network is the activity in the neuralnetwork that matches the patterns indicative of complexity in theactivity, only the output that characterizes activity that matchesrelatively more complex or higher dimensional activity would be used toreconstruct the input. By way of example, in reference to patterns 500(FIG. 5), in some implementations only activity that matches patterns515, 520, 525, and 530 could be used to reconstruct the input, whereasactivity that matches patterns 505, 510 could be ignored or discarded.In this way, a lossy reconstruction can proceed in selectedcircumstances.

In some implementations, processes 1000, 1100 can be used forpeer-to-peer encrypted communications. In particular, both the sender(i.e., the encoder) and the receiver (i.e., the decoder) can be providedwith the same recurrent artificial neural network. There are severalways in which the shared recurrent artificial neural network can betailored to ensure that a third party cannot reverse-engineer it anddecrypt the signal, including:

-   -   the structure of the recurrent artificial neural network    -   the functional settings of the recurrent artificial neural        network, including node states and edge weights,    -   the size (or dimension) of the patterns, and    -   the fraction of patterns in each dimension.        These parameters can be thought of multiple layers that together        ensure transmission security. Further, in some implementations,        the decision moment time points can be used as keys to decrypt        the signal.

Although processes 1000, 1100 are presented in terms of encoding anddecoding a single recurrent artificial neural network, processes 1000,1100 can also be applied in systems and processes that rely uponmultiple recurrent artificial neural networks. These recurrentartificial neural networks can operate either in parallel or in series.

As an example of series operation, the output of a first recurrentartificial neural network can serve as the input of a second recurrentartificial neural network. The resultant output of the second recurrentartificial neural network is a twice encoded (or twice encrypted)version of the input into the first recurrent artificial neural network.Such a series arrangement of recurrent artificial neural networks can beuseful in circumstances where different parties have different levels ofaccess to information, e.g., in medical record systems where patientidentity information may not be accessible to a party that will be usingand have access to the remainder of the medical record.

As an example of parallel operation, the same information can be inputinto multiple, different recurrent artificial neural networks. Thedifferent outputs of those neural networks can be used, e.g., to ensurethat the input can be reconstructed with high fidelity.

Although many implementations have been described, various modificationsmay be made. For example, although the application implies generallythat activity within a recurrent artificial neural network should matcha pattern indicative of ordering, this is not necessarily the case.Rather, in some implementations, activity within a recurrent artificialneural network can comport with a pattern without necessarily displayingactivity that matches the pattern. For example, an increase in thelikelihood that a recurrent neural network is to display activity thatwould match a pattern can be treated as non-random ordering of theactivity.

As yet another example, in some implementations, different groups ofpatterns can be tailored for use in characterizing the activity indifferent recurrent artificial neural networks. The patterns can betailored, e.g., according to the effectiveness of the patterns incharacterizing the activity of the different recurrent artificial neuralnetworks. Effectiveness can be quantified, e.g., based on the size of atable or vector that represents the occurrence counts of differentpatterns.

As yet another example, in some implementations, the patterns used tocharacterize the activity in a recurrent artificial neural networks canconsider the strength of a connection between nodes. In other words, thepatterns described previously herein treat all signal transmissionactivity between two nodes in a binary manner, i.e., either the activityexists or it doesn't. This is not necessarily the case. Rather, in someimplementations, comporting with a pattern can require activity of acertain level or strength of connection to be taken as indicative ofordered complexity in the activity of a recurrent artificial neuralnetwork.

As yet another example, the content of the output of the recurrentartificial neural network can include activity patterns that occuroutside windows of time in which the activity in a neural network has adistinguishable level of complexity. For example, the output of therecurrent artificial neural network that is read at 1015 and transmittedor stored at 1020 (FIG. 10) can include information encoding activitypatterns that occur, e.g., outside dashed rectangles 915, 920, 925 ingraph 905 (FIG. 9). By way of example, the output of the recurrentartificial neural network could characterize only the highestdimensional patterns of activity, regardless of when those patterns ofactivity occur. As another example, the output of the recurrentartificial neural network could characterize only patterns of activitythat enclose cavities, regardless of when those patterns of activityoccur.

FIGS. 12, 13, and 13 are schematic illustrations of an identical binaryform or representation 1200 of topological structures such as, e.g.,patterns of activity in a neural network. The topological structuresillustrated in FIGS. 12, 13, and 13 all include the same information,namely, an indication the presence or absence of features in a graph.The features can be, e.g., activity in a neural network device. In someimplementations, the activity is identified based on or during periodsof time in which the activity in the neural network has a complexitythat is distinguishable from other activity that is responsive to aninput.

As shown, binary representation 1200 includes bits 1205, 1207, 1211,1293, 1294, 1297 and an additional, arbitrary number of bits(represented by the ellipses “ . . . ”). For didactic purposes, bits1205, 1207, 1211, 1293, 1294, 1297 . . . are illustrated as discreterectangular shapes that are either filled or unfilled to indicate thebinary value of the bit. In the schematic illustrations, representation1200 superficially appears to be either a one-dimensional vector of bits(FIGS. 12, 13) or a two-dimensional matrix of bits (FIG. 14). However,representation 1200 differs from a vector, from a matrix, or from otherordered collection of bits in that the same information can be encodedregardless of the order of the bits—i.e., regardless of the location ofindividual bits within the collection.

For example, in some implementations, each individual bit 1205, 1207,1211, 1293, 1294, 1297 . . . can represent the presence or absence of atopological feature—regardless of the location of that feature in thegraph. By way of example, referring to FIG. 2, a bit such as bit 1207can indicate the presence of a topological feature that comports withpattern 505 (FIG. 5), regardless of whether that activity occurs betweennodes 104, 105, 101 or between nodes 105, 101, 102. Thus, although eachindividual bit 1205, 1207, 1211, 1293, 1294, 1297, . . . can beassociated with a particular feature, the location of that feature inthe graph need not be encoded, e.g., by a corresponding location of thebit in representation 1200. In other words, in some implementations,representation 1200 may only provide an isomorphic topologicalreconstruction of the graph.

As an aside, in other implementations, it is possible that the locationof individual bits 1205, 1207, 1211, 1293, 1294, 1297, . . . does indeedencode information such as, e.g., the location of a feature in thegraph. In these implementations, the source graph can be reconstructedusing representation 1200. However, such an encoding is not necessarilypresent.

In view of the ability of a bit to represent the presence or absence ofa topological feature regardless of the location of that feature in thegraph, in FIG. 1, bit 1205 appears before bit 1207, which appears beforebit 1211 at the start of representation 1200. In contrast, in FIGS. 2and 3, the order of bits 1205, 1207, and 1211 within representation1200—and the position of bits 1205, 1207, and 1211 relative to otherbits within representation 1200—has changed. Nevertheless, binaryrepresentation 1200 remains the same—as does the set of rules oralgorithm that defines the process for encoding information in binaryrepresentation 1200. So long as the correspondence between the bit andthe feature is known, the location of the bits in the representation1200 is irrelevant.

In further detail, each bit 1205, 1207, 1211, 1293, 1294, 1297 . . .individually represents the presence or absence of a feature in a graph.A graph is a set of nodes and a set of edges between those nodes. Thenodes can correspond to objects. Examples of objects can include, e.g.,artificial neurons in a neural network, individuals in a social network,or the like. Edges can correspond to some relation between the objects.Examples of relations include, e.g., a structural connection or activityalong the connection. In the context of a neural network, artificialneurons can be related by a structural connection between neurons or bytransmission of information along a structural connection. In thecontext of a social network, individuals can be related by a “friend” orother relational connection or by transmission of information (e.g., aposting) along such a connection. Edges can thus characterize relativelylong-lived structural characteristics of the set of nodes or relativelytransient activity characteristics that occur within a defined timeframe. Further, edges can either be directed or bidirectional. Directededges indicate directionality of the relation between the objects. Forexample, the transmission of information from a first neuron to a secondneuron can be represented by a directed edge that denotes the directionof transmission. As another example, in a social network, a relationalconnection may indicate that second user is to receive information fromthe first but not that the first is to receive information from thesecond. In topological terms, a graph can be expressed as a set of unitintervals [0, 1] where 0 and 1 are identified with respective nodes thatare connected by an edge.

The features whose presence or absence is indicated by bits 1205, 1207,1211, 1293, 1294, 1297 can be, e.g., a node, a set of nodes, a set ofsets of nodes, a set of edges, a set of sets of edges, and/or additionalhierarchically-more-complex features (e.g., a set of sets of sets ofnodes). Bits 1205, 1207, 1211, 1293, 1294, 1297 generally represent thepresence or absence of features that are at different hierarchicallevels. For example, bit 1205 may represent the presence or absence of anode, whereas bit 1205 may represent the presence or absence of a set ofnodes.

In some implementations, bits 1205, 1207, 1211, 1293, 1294, 1297 mayrepresent features in a graph that have a threshold level of somecharacteristic. For example, bits 1205, 1207, 1211, 1293, 1294, 1297 canrepresent not only that there is activity in a set of edges, but alsothat this activity is weighted either above or below a threshold level.The weights can, e.g., embody the training of a neural network device toa particular purpose or can be an innate characteristic of the edges.

FIGS. 5, 6, and 8 above illustrate features whose presence or absencecan be represented by bits 1205, 1207, 1211, 1293, 1294, 1297 . . . .

The directed simplices in collections 500, 600, 700 treat functional orstructural graphs as a topological space with nodes as points. Structureor activity involving one or more nodes and links that comports withsimplices in collection 500, 600, 700 can be represented in a bitregardless of the identity of the particular nodes and/or links thatparticipate in the activity.

In some implementations, only some patterns of structure or activity areidentified and/or some portion of the patterns of structure or activitythat are identified are discarded or otherwise ignored. For example,with reference to FIG. 5, structure or activity that comports with thefive point, 4-dimensional simplex pattern 515 inherently includesstructure or activity that comports with the four point, 3-dimensionaland three point, 2-dimension simplex patterns 510, 505. For example,points 0, 2, 3, 4 and points 1, 2, 3, 4 in 4-dimensional simplex pattern515 of FIG. 5 both comport with 3-dimensional simplex pattern 510. Insome implementations, simplex patterns that include fewer points—andhence are of a lower dimension—can be discarded or otherwise ignored.

As another example, only some patterns of structure or activity need beidentified. For example, in some implementations only patterns with oddnumber of points (3, 5, 7, . . . ) or even numbers of dimensions (2, 4,6, . . . ) are used.

Returning to FIGS. 12, 13, 14, the features whose presence or absence isrepresented by bits 1205, 1207, 1211, 1293, 1294, 1297 . . . may not beindependent of one another. By way of explanation, if bits 1205, 1207,1211, 1293, 1294, 1297 represent the presence or absence of OD-simplicesthat each reflect the existence or activity of a single node, then bits1205, 1207, 1211, 1293, 1294, 1297 are independent of one another.However, if bits 1205, 1207, 1211, 1293, 1294, 1297 represent thepresence or absence of higher-dimensional simplices that each reflectthe existence or activity of multiple nodes, then the informationencoded by the presence or absence of each individual feature may not beindependent of the presence or absence of the other features.

FIG. 15 schematically illustrates an example of how the presence orabsence of features that correspond to different bits are notindependent of one another. In particular, a subgraph 1500 that includesfour nodes 1505, 1510, 1515, 1520 and six directed edges 1525, 1530,1535, 1540, 1545, 1550 is illustrated. In particular, edge 1525 isdirected from node 1525 to node 1510, edge 1530 is directed from node1515 to node 1505, edge 1535 is directed from node 1520 to node 1505,edge 1540 is directed from node 1520 to node 1510, edge 1545 is directedfrom node 1515 to node 1510, edge 1550 is directed from node 1515 tonode 1520.

A single bit in representation 1200 (e.g., filled bit 1207 in FIGS. 12,13, 14) may indicate the presence of a directed 3D-simplex. For example,such a bit could indicate the presence of the 3D-simplex formed by nodes1505, 1510, 1515, 1520 and edges 1525, 1530, 1535, 1540, 1545, 1550. Asecond bit in representation 1200 (e.g., filled bit 1293 in FIGS. 12,13, 14) may indicate the presence of a directed 2D-simplex. For example,such a bit could indicate the presence of the 2D-simplex formed by nodes1515, 1505, 1510 and edges 1525, 1530, 1545. In this simple example, theinformation encoded by bit 1293 is completely redundant with theinformation encoded by bit 1207.

Please note that the information encoded by bit 1293 may also beredundant with the information encoded by still further bit. Forexample, the information encoded by bit 1293 would be redundant withboth a third bit and a fourth bit that indicates the presence ofadditional directed 2D-simplices. Examples of those simplices are formedby nodes 1515, 1520, 1510 and edges 1540, 1545, 1550 and nodes 1520,1505, 1510 and edges 1525, 1535, 1540.

FIG. 16 schematically illustrates another example of how the presence orabsence of features that correspond to different bits are notindependent of one another. In particular, a subgraph 1600 that includesfour nodes 1505, 1510, 1515, 1520 and five directed edges 1625, 1630,1635, 1640, 1645 is illustrated. Nodes 1505, 1510, 1515, 1520 and edges1625, 1630, 1635, 1640, 1645 generally correspond to nodes 1505, 1510,1515, 1520 and edges 1525, 1530, 1535, 1540, 1545 in subgraph 1500 (FIG.15). However, in contrast with subgraph 1500 in which nodes 1515, 1520are connected by edge 1550, nodes 1615, 1620 are not connected by anedge.

A single bit in representation 1200 (e.g., unfilled bit 1205 in FIGS.12, 13, 14) may indicate the absence of a directed 3D-simplex, such as,e.g., the directed 3D-simplex that encompasses nodes 1605, 1610, 1615,1620. A second bit in representation 1200 (e.g., filled bit 1293 inFIGS. 12, 13, 14) may indicate the presence of a 2D-simplex. An exampledirected 2D-simplex is formed by nodes 1615, 1605, 1610 and edges 1625,1630, 1645. This combination of a filled bit 1293 and an unfilled bit1205 provides information indicative of presence or absence of otherfeatures (and the state of other bits) that may or may not be present inrepresentation 1200. In particular, the combination of the absence of adirected 3D-simplex and the presence of a directed 2D-simplex indicatesthat at least one edge is absent from either:

a) the possible directed 2D-simplex formed by nodes 1615, 1620, 1610 or

b) the possible directed 2D-simplex formed by nodes 1620, 1605, 1610.

Thus, the state of a bit that represents the presence or absence ofeither of these possible simplices is not independent of the state ofbits 1205, 1293.

Although these examples have been discussed in terms of a features withdifferent numbers of nodes and a hierarchical relationship, this is notnecessarily the case. For example, a representation 1200 that includes acollection of bits that corresponds only to, e.g., the presence orabsence of 3D-simplices is possible.

Using individual bits to represent the presence or absence of featuresin a graph yields certain properties. For example, the encoding ofinformation is fault tolerant and provides “graceful degradation” of theencoded information. In particular, the loss of a particular bit (orgroup of bits) may increase the uncertainty as to the presence orabsence of a feature. However, estimates of the likelihood that afeature is present or absent will still be possible from the other bitsthat indicate the presence or absence of adjacent features.

Likewise, as the number of bits increases, certainty as to the presenceor absence of a feature increases.

As another example, as discussed above, the ordering or arrangement ofbits is irrelevant to isomorphic reconstruction of the graph that isrepresented by the bit. All that is required is a known correspondencebetween the bits and particular nodes/structures in the graph.

In some implementations, the patterns of activity in a neural networkcan be encoded in a representation 1200 (FIGS. 12, 13, and 13). Ingeneral, the patterns of activity in a neural network are a result of anumber of characteristics of the neural network such as, e.g., thestructural connections between nodes of the neural network, the weightsbetween nodes, as well as a whole host of possible other parameters. Forexample, in some implementations, the neural network could have beentrained prior to the encoding of the patterns of activity inrepresentation 1200.

However, regardless of whether the neural network untrained or trained,for a given input, the responsive pattern of activity can be thought ofa “representation” or an “abstraction” of that input within the neuralnetwork. Thus, although representation 1200 can appear to be astraightforward-appearing collection of (in some cases, binary) digits,each of the digits can encode the relationship or correspondence betweena particular input and relevant activity in the neural network.

FIGS. 17, 18, 19, 20 are schematic illustrations of the use ofrepresentations of the occurrence of topological structures in theactivity in a neural network in four different classification systems1700, 1800, 1900, 2000. Classification systems 1700, 1800 each classifyrepresentations of the patterns of activity in a neural network as partof the classification of input. Classification systems 1900, 2000 eachclassify approximations of representations of the patterns of activityin a neural network as part of the classification of input. Inclassification systems 1700, 1800, the patterns of activity that arerepresented occur in and are read from a source neural network device1705 that is part of the classification system 1700, 1800. In contrast,in classification systems 1900, 2000, the patterns of activity that areapproximately represented occur in a source neural network device thatis not part of the classification system 1700, 1800. Nevertheless, theapproximation of the representation of those patterns of activity areread from an approximator 1905 that is part of classification systems1900, 2000.

In additional detail, turning to FIG. 17, classification system 1700includes a source neural network 1705 and a linear classifier 1710.Source neural network 1705 is a neural network device that is configuredto receive an input and present representations of the occurrence oftopological structures in the activity within source neural network1705. In the illustrated implementation, source neural network 1705includes an input layer 1715 that receives the input. However, this isnot necessarily the case. For example, in some implementation, some orall of the input can be injected into different layers and/or edges ornodes throughout source neural network 1705.

Source neural network 1705 can be any of variety of different types ofneural network. In general, source neural network 1705 is a recurrentneural network such as, e.g., a recurrent neural network that ismodelled on a biological system. In some cases, source neural network1705 can model a degree of the morphological, chemical, and othercharacteristics of a biological system. In general, source neuralnetwork 1705 is implemented on one or more computing devices with arelatively high level of computational performance, e.g., asupercomputer. In such cases, classification system 1700 will generallybe a dispersed system in which a remote classifier 1710 communicateswith source neural network 1705, e.g., via a data communicationsnetwork.

In some implementations, source neural network 1705 can be untrained andthe activity that is represented can be the innate activity of sourceneural network 1705. In other implementations, source neural network1705 can be trained and the activity that is represented can embody thistraining.

The representations read from source neural network 1705 can berepresentations such as representation 1200 (FIGS. 12, 13, 14). Therepresentations can be read from source neural network 1705 in a numberof ways. For example, in the illustrated example, source neural network1705 includes “reader nodes” that read patterns of activity betweenother nodes within source neural network 1705. In other implementations,the activity within source neural network 1705 are read by a dataprocessing component of that is programmed to monitor source neuralnetwork 1705 for relatively highly-ordered patterns of activity. Instill other implementations, source neural network 1705 can include anoutput layer from which representation 1200 can be read, e.g., whensource neural network 1705 is implemented as a feed-forward neuralnetwork.

Linear classifier 1710 is a device that classifies an object—namely,representations of the patterns of activity in source neural network1705—based on a linear combination of the object's characteristics.Linear classifier 1710 includes an input 1720 and an output 1725. Input1720 is coupled to receive representations of the patterns of activityin source neural network 1705. In other words, the representations ofthe patterns of activity in source neural network 1705 is a featurevector that represents the characteristics of the input into sourceneural network 1705 that are used by linear classifier 1710 to classifythat input. Linear classifier 1710 can receive the representations ofthe patterns of activity in source neural network 1705 in a variety ofways. For example, the representations of the patterns of activity canbe received as discrete events or as a continuous stream over a realtime or non-real time communication channel.

Output 1725 is coupled to output the classification result from linearclassifier 1710. In the illustrated implementation, output 1725 isschematically illustrated as a parallel port with multiple channels.This is not necessarily the case. For example, output 1725 can outputclassification result over a serial port or a port with combinedparallel and serial capabilities.

In some implementations, linear classifier 1710 can be implemented onone or more computing devices with relatively limited computationalperformance. For example, linear classifier 1710 can be implemented on apersonal computer or a mobile computing device such as a smart phone ortablet.

In FIG. 18, classification system 1800 includes source neural network1705 and a neural network classifier 1810. Neural network classifier1810 is a neural network device classifies an object—namely,representations of the patterns of activity in source neural network1705—based on a non-linear combination of the object's characteristics.In the illustrated implementation, neural network classifier 1810 is afeedforward network that includes an input layer 1820 and an outputlayer 1825. As with linear classifier 1710, neural network classifier1810 can receive the representations of the patterns of activity insource neural network 1705 in a variety of ways. For example, therepresentations of the patterns of activity can be received as discreteevents or as a continuous stream over a real time or non-real timecommunication channel.

In some implementations, neural network classifier 1810 can performinferences on one or more computing devices with relatively limitedcomputational performance. For example, neural network classifier 1810can be implemented on a personal computer or a mobile computing devicesuch as a smart phone or tablet, e.g., in a Neural Processing Unit ofsuch a device. Like classification system 1700, classification system1800 will generally be a dispersed system in which a remote neuralnetwork classifier 1810 communicates with source neural network 1705,e.g., via a data communications network.

In some implementations, neural network classifier 1810 can be, e.g., adeep neural network such as a convolutional neural network that includesconvolutional layers, pooling layers, and fully-connected layers.Convolutional layers can generate feature maps, e.g., using linearconvolutional filters and/or nonlinear activation functions. Poolinglayers reduce the number of parameters and control overfitting. Thecomputations performed by the different layers in image classifier 1820can be defined in different ways in different implementations of imageclassifier 1820.

In FIG. 19, classification system 1900 includes source approximator 1905and a linear classifier 1710. As discussed further below, sourceapproximator 1905 is a relatively simple neural network that is trainedto receive input—either at an input layer 1915 or elsewhere—and output avector that approximates a representation of topological structures thatarise in the patterns of activity in a relatively more complex neuralnetwork. For example, source approximator 1905 can be trained toapproximate a recurrent source neural network such as, e.g., a recurrentneural network that is modelled on a biological system and includes adegree of the morphological, chemical, and other characteristics of abiological system. In the illustrated implementation, sourceapproximator 1905 includes an input layer 1915 and an output layer 1920.Input layer 1915 is couplable to receive the input data. Output layer1920 is coupled to output an approximation of a representation of theactivity within a neural network device for receipt by input 1720 oflinear classifier. For example, output layer 1920 can output anapproximation 1200′ of representation 1200 (FIGS. 12, 13, 14). As anaside, the representation 1200 schematically illustrated in FIGS. 17, 18and the approximation 1200′ of representation 1200 schematicallyillustrated in FIGS. 19, 20 are identical. This is for the sake ofconvenience only. In general, approximation 1200′ will differ fromrepresentation 1200 in at least some ways. Despite those differences,linear classifier 1710 can classify approximation 1200′.

In general, source approximator 1905 can perform inferences on one ormore computing devices with relatively limited computationalperformance. For example, source approximator 1905 can be implemented ona personal computer or a mobile computing device such as a smart phoneor tablet, e.g., in a Neural Processing Unit of such a device. Ingeneral and in contrast with classification systems 1700, 1800,classification system 1900 will generally be housed in a single housing,e.g., with source approximator 1905 and linear classifier 1710implemented on either the same data processing devices or on dataprocessing devices coupled by a hardwired connection.

In FIG. 20, classification system 2000 includes source approximator 1905and a neural network classifier 1810. Output layer 1920 of sourceapproximator 1905 is coupled to output an approximation 1200′ of arepresentation of the activity within a neural network device forreceipt by input 1820 of neural network classifier 1810. Despite anydifferences between approximation 1200′ and representation 1200, neuralnetwork classifier 1810 can classify approximation 1200′. In general andlike classification system 1900, classification system 1900 willgenerally be housed in a single housing, e.g., with source approximator1905 and neural network classifier 1810 implemented on either the samedata processing devices or on data processing devices coupled by ahardwired connection.

FIG. 21 is a schematic illustration of an edge device 2100 that includesa local artificial neural network that can be trained usingrepresentations of the occurrence of topological structurescorresponding to activity in a source neural network. In this context, alocal artificial neural network can be, e.g., an artificial neuralnetwork that is executed entirely on one or more local processors thatdo not require a communications network to exchange data. In general,the local processors will be connected by hardwire connections. In someinstances, the local processors can be housed in a single housing, suchas a single personal computer or a single handheld, mobile device. Insome instances, the local processors can be under the control of andaccessible by a single individual or a limited number of individuals. Ineffect, by using a representation of the occurrence of topologicalstructures in a more complex source neural network to train (e.g., usinga supervised learning or reinforcement learning technique) a simplerand/or less highly trained but more exclusive second neural network,even individuals with limited computing resources and limited numbers oftraining samples can train a neural network as desired. Storagerequirements and computational complexity during training are reducedand resources like battery lifespan are spared.

In the illustrated implementation, edge device 2100 is schematicallyillustrated as a security-camera device that includes an optical imagingsystem 2110, image processing electronics 2115, a source approximator2120, a representation classifier 2125, and a communications controllerand interface 2130.

Optical imaging system 2110 can include, e.g., one or more lenses (oreven a pinhole) and a CCD device. Image processing electronics 2115 canread the output of optical imaging system 2110 and in general canperform basic image processing functions. Communications controller andinterface 2130 is a device that is configured to control the flow ofinformation to and from device 2100. As discussed further below, amongthe operations that communications controller and interface 2130 canperform are the transmission of images of interest to other devices andthe receipt of training information from other devices. Communicationscontroller and interface 2130 can thus include both a data transmitterand a receiver that can communicate over, e.g., a data port 2135. Dataport 2135 can be a wired port, a wireless port, an optical port, or thelike.

Source approximator 2120 is relatively simple neural network that istrained to output a vector that approximates a representation oftopological structures that arise in the patterns of activity in arelatively more complex neural network. For example, source approximator2120 can be trained to approximate a recurrent source neural networksuch as, e.g., a recurrent neural network that is modelled on abiological system and includes a degree of the morphological, chemical,and other characteristics of a biological system.

Representation classifier 2125 is either a linear classifier or a neuralnetwork classifier that is coupled to receive an approximation of arepresentation of the patterns of activity in a source neural networkfrom source approximator 2120 and output a classification result.Representation classifier 2125 can be, e.g., a deep neural network suchas a convolutional neural network that includes convolutional layers,pooling layers, and fully-connected layers. Convolutional layers cangenerate feature maps, e.g., using linear convolutional filters and/ornonlinear activation functions. Pooling layers reduce the number ofparameters and control overfitting. The computations performed by thedifferent layers in representation classifier 2125 can be defined indifferent ways in different implementations of representation classifier2125.

In some implementations, in operation, optical imaging system 2110 cangenerate raw digital images. Image processing electronics 2115 can readthe raw images and will generally perform at least some basic imageprocessing functions. Source approximator 2120 can receive images fromimage processing electronics 2115 and perform inference operations tooutput a vector that approximates a representation of topologicalstructures that arise in the patterns of activity in a relatively morecomplex neural network. This approximation vector is input intorepresentation classifier 2125 which determines whether theapproximation vector satisfies one or more sets of classificationcriteria. Examples include facial recognition and other machine visionoperations. In the event that representation classifier 2125 determinesthat the approximation vector satisfies a set of classificationcriteria, representation classifier 2125 can instruct communicationscontroller and interface 2130 to transmit information regarding theimages. For example, communications controller and interface 2130 cantransmit the image itself, the classification, and/or other informationregarding the images.

At times, it may be desirable to change the classification process. Inthese cases, communications controller and interface 2130 can receive atraining set. In some implementations, the training set can include rawor processed image data and representations of topological structuresthat arise in the patterns of activity in a relatively more complexneural network. Such a training set can be used to retrain sourceapproximator 2120, e.g., using a supervised learning or reinforcementlearning technique. In particular, the representations are used as thetarget answer vectors and represent the desired result of sourceapproximator 2120 processing the raw or processed image data.

In other implementations, the training set can include representationsof topological structures that arise in the patterns of activity in arelatively more complex neural network and the desired classification ofthose representations of topological structures. Such a training set canbe used to retrain a neural network representation classifier 2125,e.g., using a supervised learning or reinforcement learning technique.In particular, the desired classification are used as the target answervectors and represent the desired result of representation classifier2125 processing the representations of topological structures.

Regardless of whether source approximator 2120 or representationclassifier 2125 is retrained, inference operations at device 2100 can befacilely adapted to changing circumstances and objectives without largesets of training data and time- and computing power-intensive iterativetraining.

FIG. 22 is a schematic illustration of a second edge device 2200 thatincludes a local artificial neural network that can be trained usingrepresentations of the occurrence of topological structurescorresponding to activity in a source neural network. In the illustratedimplementation, second edge device 2200 is schematically illustrated asa mobile computing device such as smart phone or a tablet. Device 2200includes an optical imaging system (e.g., on the backside of device2200, not shown), image processing electronics 2215, a representationclassifier 2225, a communications controller and interface 2230, and adata port 2235. These components can have characteristics and performactions that correspond to those of optical imaging system 2110, imageprocessing electronics 2115, representation classifier 2125,communications controller and interface 2130, and data port 2135 indevice 2100 (FIG. 21).

The illustrated implementation of device 2200 additionally includes oneor more additional sensors 2240 and a multi-input source approximator2245. Sensor(s) 2240 can sense one of more characteristics of theenvironment surrounding device 2200 or of device 2200 itself. Forexample, in some implementations, sensor 2240 can be an accelerometerthat senses the acceleration to which device 2200 is subject. As anotherexample, in some implementations, sensor 2240 can be an acoustic sensorsuch as a microphone that senses noise in the environment of device2200. Still further examples of sensor 2240 include chemical sensors(e.g., “artificial noses” and the like), humidity sensors, radiationsensors, and the like. In some cases, sensor 2240 is coupled toprocessing electronics that can read the output of sensor 2240 (or otherinformation such as, e.g., a contact list or map) and perform basicprocessing functions. Different implementations of sensor 2240 can thushave different “modalities” in that the physical sensed physicalparameter changes from sensor to sensor.

Multi-input source approximator 2245 is a relatively simple neuralnetwork that is trained to output a vector that approximates arepresentation of topological structures that arise in the patterns ofactivity in a relatively more complex neural network. For example,multi-input source approximator 2245 can be trained to approximate arecurrent source neural network such as, e.g., a recurrent neuralnetwork that is modelled on a biological system and includes a degree ofthe morphological, chemical, and other characteristics of a biologicalsystem.

Unlike source approximator 2120, multi-input source approximator 2245 iscoupled to receive raw or processed sensor data from multiple sensorsand return an approximation of a representation of topologicalstructures that arise in the patterns of activity in a relatively morecomplex neural network based on that data. For example, multi-inputsource approximator 2245 can receive processed image data from imageprocessing electronics 2215 as well as, e.g., acoustic, acceleration,chemical, or other data from one or more sensors 2240. Multi-inputsource approximator 2245 can be, e.g., a deep neural network such as aconvolutional neural network that includes convolutional layers, poolinglayers, and fully-connected layers. The computations performed by thedifferent layers in multi-input source approximator 2245 can bededicated to a single type of sensor data or to sensor data of multiplemodalities.

Regardless of the particular organization of multi-input sourceapproximator 2245, multi-input source approximator 2245 is to return theapproximation based on raw or processed sensor data from multiplesensors. For example, processed image data from image processingelectronics 2215 and acoustic data from a microphone sensor 2240 can beused by multi-input source approximator 2245 to approximate arepresentation of topological structures that would arise in thepatterns of activity in a relatively more complex neural network thatreceived the same data.

At times, it may be desirable to change the classification process atdevice 2200. In these cases, communications controller and interface2230 can receive a training set. In some implementations, the trainingset can include raw or processed image, sounds, chemical or other dataand representations of topological structures that arise in the patternsof activity in a relatively more complex neural network. Such a trainingset can be used to retrain multi-input source approximator 2245, e.g.,using a supervised learning or reinforcement learning technique. Inparticular, the representations are used as the target answer vectorsand represent the desired result of multi-input source approximator 2245processing the raw or processed image or sensor data.

In other implementations, the training set can include representationsof topological structures that arise in the patterns of activity in arelatively more complex neural network and the desired classification ofthose representations of topological structures. Such a training set canbe used to retrain a neural network representation classifier 2225,e.g., using a supervised learning or reinforcement learning technique.In particular, the desired classification are used as the target answervectors and represent the desired result of representation classifier2225 processing the representations of topological structures.

Regardless of whether multi-input source approximator 2245 orrepresentation classifier 2225 is retrained, inference operations atdevice 2200 can be facilely adapted to changing circumstances andobjectives without large sets of training data and time- and computingpower-intensive iterative training.

FIG. 23 is a schematic representation of a system 2300 in which localneural networks can be trained using representations of the occurrenceof topological structures corresponding to activity in a source neuralnetwork. The target neural networks are implemented on relativelysimple, less expensive data processing systems whereas the source neuralnetwork can implemented on a relatively complex, more expensive dataprocessing system.

System 2300 includes a variety of devices 2305 with local neuralnetworks, a telephone base station 2310, a wireless access point 2315, aserver system 2320, and one or more data communications networks 2325.

Local neural networks devices 2305 are devices that are configured toprocess data using computationally-less-intensive target neuralnetworks. As illustrated, local neural networks devices 2305 can beimplemented as mobile computing devices, cameras, automobiles, or any ofa legion of other appliances, fixtures, and mobile components, as wellas different makes and models of devices within each category. Differentlocal neural networks devices 2305 can belong to different owners. Insome implementations, access to the data processing functionality oflocal neural networks devices 2305 will generally be restricted to theseowners and/or the owner's designates.

Local neural networks devices 2305 can each include one or more sourceapproximators that are trained to output a vector that approximates arepresentation of topological structures that arise in the patterns ofactivity in a relatively more complex neural network. For example, therelatively more complex neural network can be a recurrent source neuralnetwork such as, e.g., a recurrent neural network that is modelled on abiological system and includes a degree of the morphological, chemical,and other characteristics of a biological system.

In some implementations, in addition to processing data using sourceapproximators, local neural networks devices 2305 can also be programmedto re-train the source approximators using representations oftopological structures that arise in the patterns of activity in arelatively more complex neural network as the target answer vectors. Forexample, local neural networks devices 2305 can be programmed to performone or more iterative training techniques (e.g., gradient descent orstochastic gradient descent). In other implementations, the sourceapproximators in local neural networks devices 2305 are trainable by,e.g., a dedicated training system or by a training system that isinstalled on a personal computer that can interact with the local neuralnetworks devices 2305 to train source approximators.

Each local neural networks device 2305 includes one or more wireless orwired data communication components. In the illustrated implementation,each local neural networks device 2305 includes at least one wirelessdata communication components such as a mobile phone transceiver, awireless transceiver, or both. The mobile phone transceivers are able toexchange data with phone base station 2310. The wireless transceiversare able to exchange data with a wireless access point 2315. Each localneural networks device 2305 may also be able to exchange data with peermobile computing devices.

Phone base station 2310 and wireless access point 2315 are connected fordata communication with one or more data communication networks 2325 andcan exchange information with a server system 2320 over the network(s).Local neural networks devices 2305 are thus generally also in datacommunication with server system 2320. However, this is not necessarilythe case. For example, in implementations where local neural networksdevices 2305 are trained by other data processing devices, local neuralnetworks devices 2305 need only be in data communication with theseother data processing devices at least once.

Server system 2320 is a system of one or more data processing devicesthat is programmed to perform data processing activities in accordancewith one or more sets of machine-readable instructions. The activitiescan include serving training sets to training systems for mobilecomputing devices 2305. As discussed above, the training systems can beinternal to mobile local neural networks devices 2305 themselves or onone or more other data processing devices. The training sets can includerepresentations of the occurrence of topological structurescorresponding to activity in a source neural network and correspondinginput data.

In some implementations, server system 2320 also includes the sourceneural network. However, this is not necessarily the case and serversystem 2320 may receive the training sets from yet another system ofdata processing device(s) that implement the source neural network.

In operations, after server system 2320 receives a training set (from asource neural network that is found at server system 2320 itself orelsewhere), server system 2320 can serve the training set to trainersthat train mobile computing devices 2305. The source approximators intarget local neural networks devices 2305 can be trained using thetraining set so that the target neural networks approximate theoperations of the source neural network.

FIGS. 24, 25, 26, 27 are schematic illustrations of the use ofrepresentations of the occurrence of topological structures in theactivity in a neural network in four different systems 2400, 2500, 2600,2700. Systems 2400, 2500, 2600, 2700 can be configured to perform any ofa number of different operations. For example, systems 2400, 2500, 2600,2700 can perform object localization operations, object detectionoperations, object segmentation operations, object detection operations,prediction operations, action selection operations, or the like.

Object localization operations locate an object within an image. Forexample, a bounding box can be constructed around an object. In somecases, object localization can be combined with object recognition, inwhich the localized object is labeled with an appropriate designation.

Object detection operations classify image pixels as either belonging toa particular class (e.g., belonging to an object interest) or not. Ingeneral, object detection is performed by grouping pixels and formingbounding boxes around the pixel groups. The bounding box should be atight fit around the object.

Object segmentation generally assigns class labels to each image pixel.Thus, rather than a bounding box, object segmentation proceeds on apixel-by-pixel basis and generally requires that only a single label beassigned to each pixel.

Prediction operations seek to draw conclusions that are outside therange of a observed data. Although prediction operations can seek toforecast future occurrences (e.g., based on information about the pastand current state), prediction operations can also seek to drawconclusions about the past and current state based on incompleteinformation on those states.

Action selection operations seek to choose an action based on a set ofconditions. Action selection operations have traditionally be brokendown into different approaches such a symbol-based systems (classicalplanning), distributed solutions, and reactive or dynamic planning.

Classification systems 2400, 2500 each perform a desired operation onrepresentations of the patterns of activity in a neural network. Systems2600, 2700 each perform a desired operation on approximations ofrepresentations of the patterns of activity in a neural network. Insystems 2400, 2500, the patterns of activity that are represented occurin and are read from a source neural network device 1705 that is part ofthe system 2400, 2500. In contrast, in systems 2400, 2500, the patternsof activity that are approximately represented occur in a source neuralnetwork device that is not part of the system 2400, 2500. Nevertheless,the approximation of the representation of those patterns of activityare read from an approximator 1905 that is part of systems 2400, 2500.

In additional detail, turning to FIG. 24, system 2400 includes a sourceneural network 1705 and a linear processor 2410. Linear processor 2410is a device that performs operations based on a linear combination ofthe characteristics of representations of the patterns of activity in aneural network (or approximations of such representations). Theoperations can be, e.g., object localization operations, objectdetection operations, object segmentation operations, object detectionoperations, prediction operations, action selection operations, or thelike.

Linear processor 2410 includes an input 2420 and an output 2425. Input2420 is coupled to receive representations of the patterns of activityin source neural network 1705. Linear processor 2410 can receive therepresentations of the patterns of activity in source neural network1705 in a variety of ways. For example, the representations of thepatterns of activity can be received as discrete events or as acontinuous stream over a real time or non-real time communicationchannel. Output 2525 is coupled to output the processing result fromlinear processor 2410. In some implementations, linear processor 2410can be implemented on one or more computing devices with relativelylimited computational performance. For example, linear processor 2410can be implemented on a personal computer or a mobile computing devicesuch as a smart phone or tablet.

Turning to FIG. 24, system 2400 includes a source neural network 1705and a linear processor 2410. Linear processor 2410 is a device thatperforms operations based on a linear combination of the characteristicsof representations of the patterns of activity in a neural network (orapproximations of such representations). The operations can be, e.g.,object localization operations, object detection operations, objectsegmentation operations, prediction operations, action selectionoperations, or the like.

Linear processor 2410 includes an input 2420 and an output 2425. Input2420 is coupled to receive representations of the patterns of activityin source neural network 1705. Linear processor 2410 can receive therepresentations of the patterns of activity in source neural network1705 in a variety of ways. For example, the representations of thepatterns of activity can be received as discrete events or as acontinuous stream over a real time or non-real time communicationchannel. Output 2525 is coupled to output the processing result fromlinear processor 2410. In some implementations, linear processor 2410can be implemented on one or more computing devices with relativelylimited computational performance. For example, linear processor 2410can be implemented on a personal computer or a mobile computing devicesuch as a smart phone or tablet.

In FIG. 25, classification system 2500 includes source neural network1705 and a neural network 2510. Neural network 2510 is a neural networkdevice that is configured to perform operations based on a non-linearcombination of the characteristics of representations of the patterns ofactivity in a neural network (or approximations of suchrepresentations). The operations can be, e.g., object localizationoperations, object detection operations, object segmentation operations,prediction operations, action selection operations, or the like. In theillustrated implementation, neural network 2510 is a feedforward networkthat includes an input layer 2520 and an output layer 2525. As withlinear processor 2410, neural network 2510 can receive therepresentations of the patterns of activity in source neural network1705 in a variety of ways.

In some implementations, neural network 2510 can perform inferences onone or more computing devices with relatively limited computationalperformance. For example, neural network 2510 can be implemented on apersonal computer or a mobile computing device such as a smart phone ortablet, e.g., in a Neural Processing Unit of such a device. Like system2400, system 2500 will generally be a dispersed system in which a remoteneural network 2510 communicates with source neural network 1705, e.g.,via a data communications network. In some implementations, neuralnetwork 2510 can be, e.g., a deep neural network such as a convolutionalneural network.

In FIG. 26, system 2600 includes source approximator 1905 and a linearprocessor 2410. Despite any differences between approximation 1200′representation 1200, processor 2410 can still perform operations onapproximation 1200′.

In FIG. 27, system 2700 includes source approximator 1905 and a neuralnetwork 2510. Despite any differences between approximation 1200′ andrepresentation 1200, neural network 2510 can still perform operations onapproximation 1200′.

In some implementations, systems 2600, 2700 can be implemented on anedge device, such as, e.g., edge devices 2100, 2200 (FIGS. 21, 22). Insome implementations, systems 2600, 2700 can be implemented as part of asystem in which local neural networks can be trained usingrepresentations of the occurrence of topological structurescorresponding to activity in a source neural network, such as system2300 (FIG. 23).

FIG. 28 is a schematic illustration of a reinforcement learning system2800 that includes an artificial neural network that can be trainedusing representations of the occurrence of topological structurescorresponding to activity in a source neural network. Reinforcementlearning is a type of machine learning in which an artificial neuralnetwork learns from feedback regarding the consequences of actions takenin response to the artificial neural network's decisions. Areinforcement learning system moves from one state in the environment toanother by performing actions and receiving information characterizingthe new state and a reward and/or regret that characterizes the success(or lack of success) of the action. Reinforcement learning seek tomaximize the total reward (or minimize the regret) through a learningprocess.

In the illustrated implementation, the artificial neural network inreinforcement learning system 2800 is a deep neural network 2805 (orother deep learning architecture) that is trained using a reinforcementlearning approach. In some implementations, deep neural network 2805 canbe a local artificial neural network (such as neural network 2510 (FIGS.25, 27), and implemented locally on, e.g., an automobile, a plane, arobot, or other device. However, this is not necessarily the case and inother implementations, deep neural network 2805 can be implemented on asystem of networked devices.

In addition to a source approximator 1905 and deep neural network 2805,reinforcement learning system 2800 includes an actuator 2810, one ormore sensors 2815, and a teacher module 2820. In some implementations,reinforcement learning system 2800 also includes one or more sources2825 of additional data.

Actuator 2810 is a device that controls a mechanism or a system thatinteracts with an environment 2830. In some implementations, actuator2810 controls a physical mechanism or system (e.g., the steering of anautomobile or the positioning of a robot). In other implementations,actuator 2810 can control a virtual mechanism or system (e.g., a virtualgame board or an investment portfolio). Thus, environment 2830 may alsobe physical or virtual.

Sensor(s) 2815 are devices that measure characteristics of theenvironment 2830. At least some of the measurements with characterizeinteractions between the controlled mechanism or system and otheraspects of the environment 2830. For example, when actuator 2810 steersan automobile, sensor(s) 2815 may measure one or more of the speed,direction, and acceleration of the automobile, the proximity of theautomobile to other features, and the response of other features to theautomobile. As another example, when actuator 2810 controls aninvestment portfolio, sensor(s) 2815 may measure the value and riskassociated with the portfolio.

In general, both source approximator 1905 and teacher module 2820 arecoupled to receive at least some of the measurements made by sensor(s)2815. For example, source approximator 1905 can receive measurement dataat input layer 1915 and output an approximation 1200′ of arepresentation of topological structures that arise in the patterns ofactivity in a source neural network.

Teacher module 2820 is a device that is configured to interpret themeasurements received from sensor(s) 2815 and provide a reward and/or aregret to deep neural network 2805. Rewards are positive and indicatesuccessful control of the mechanism or system. Regrets are negative andindicate unsuccessful or less than optimal control. In general, teachermodule 2820 also provides a characterization of the measurements alongwith the reward/regret for reinforcement learning. In general, thecharacterization of the measurements is an approximation of arepresentation of topological structures that arise in the patterns ofactivity in a source neural network (such as approximation 1200′). Forexample, teacher module 2820 may read approximations 1200′ output fromsource approximator 1905 and pair the read approximations 1200′ withcorresponding reward/regret values.

In many implementations, reinforcement learning does not occur in system2800 in real time or during active control of actuator 2810 by deepneural network 2805. Rather, training feedback can be collected byteacher module 2820 and used for reinforcement training when deep neuralnetwork 2805 is not actively instructing actuator 2810. For example, insome implementations, teacher module 2820 can be remote from deep neuralnetwork 2805 and only in intermittent data communication with deepneural network 2805. Regardless of whether reinforcement learning isintermittent or continuous, deep neural network 2805 can be evolved,e.g., to optimize reward and/or reduce regret using the informationreceived from teacher module 2820.

In some implementations, system 2800 also includes one or more sources2825 of additional data. Source approximator 1905 can also receive datafrom data sources 2825 at input layer 1915. In these instances,approximation 1200′ will result from processing both sensor data and thedata from data sources 2825.

In some implementations, the data collected by one reinforcementlearning system 2800 can be used for training or reinforcement learningof other systems, including other reinforcement learning systems. Forexample, the characterization of the measurements along with thereward/regret values can be provided by teacher module 2820 to a dataexchange system that collects such data from a variety of reinforcementlearning systems and redistributes it among them. Further, as discussedabove, the characterization of the measurements can be an approximationof a representation of topological structures that arise in the patternsof activity in a source neural network, such as approximation 1200′.

The particular operations that are performed by reinforcement learningsystem 2800 will of course depend on the particular operational context.For example, in contexts where source approximator 1905, deep neuralnetwork 2805, actuator 2810, and sensors 2815 are part of an automobile,deep neural network 2805 can perform object localization and/ordetection operations while steering the automobile.

In implementations where the data collected by reinforcement learningsystem 2800 is used for training or reinforcement learning of othersystems, reward/regret values and approximations 1200′ that characterizethe state of the environment when object localizations and/or detectionoperations were performed can be provided to the data exchange system.The data exchange system can then distribute the reward/regret valuesand approximations 1200′ to other reinforcement learning systems 2800associated with other vehicles for reinforcement learning at those othervehicles. For example, reinforcement learning can be used to improveobject localization and/or detection operations at a second vehicleusing the reward/regret values and approximations 1200′.

However, the operations that are learned at other vehicles need not beidentical to the operations that are performed by deep neural network2805. For example, reward/regret values that are based on travel timeand approximations 1200′ that result from the input of sensor datacharacterizing, e.g., an unexpectedly wet road at a location identifiedby a GPS data source 2825 can be can be used for route planningoperations at another vehicle.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made. For example, althoughrepresentation 1200 is a binary representation in which each bitindividually represents the presence or absence of a feature in a graph,other representations of information are possible. For example, a vectoror matrix of multi-valued, non-binary digits can be used to represent,e.g., the presence or absence of features and possibly othercharacteristics of those features. An example of such a characteristicis the weight of edges with the activity that constitutes the features.

Accordingly, other embodiments are within the scope of the followingclaims.

1. A method for identifying decision moments in a recurrent artificialneural network, the method comprising: determining a complexity ofpatterns of activity in the recurrent artificial neural network;determining a timing of activity having a complexity that isdistinguishable from complexity of other activity that is responsive tothe input; and identifying a decision moment based on the timing of theactivity that has the distinguishable complexity.
 2. The method of claim1, wherein determining the complexity of patterns comprises determininga number of patterns enclosing cavities.
 3. The method of claim 1,further comprising estimating whether the activity is responsive to theinput into the recurrent artificial neural network, the estimatingcomprising: estimating that relatively simpler patterns of activityrelatively soon after the input event are responsive the input but thatrelatively more complex patterns of activity relatively soon after theinput event are not responsive the input; and estimating that relativelymore complex patterns of activity relatively later after the input eventare responsive the input but that relatively simpler patterns ofactivity relatively later after the input event are not responsive theinput.
 4. The method of claim 1, wherein determining the timing of theactivity having the distinguishable complexity comprises determining thetiming of activity having relatively high complexity indicative ofordered activity within the recurrent artificial neural network, whereinthe relatively high complexity is determined relative to a baselinelevel of activity or to an average level of activity responsive to aplurality of different inputs.
 5. The method of claim 1, whereindetermining the complexity of the patterns of activity comprisesidentifying clique patterns of activity of the recurrent artificialneural network.
 6. The method of claim 3, wherein the method furthercomprises defining a plurality of windows of time during which theactivity of the recurrent artificial neural network is responsive to aninput into the recurrent artificial neural network, wherein the cliquepatterns of activity are identified in each of the pluralities ofwindows of time.
 7. The method of claim 4, wherein the method furthercomprises identifying a first window of time within the plurality ofwindows of time based on a distinguishable likelihood of the cliquepatterns of activity being identified during the first window.
 8. Themethod of claim 3, wherein identifying clique patterns comprisesidentifying groups of directed cliques that enclose cavities.
 9. Themethod of claim 5, wherein determining the complexity of the patterns ofactivity comprises weighting a number of occurrences of the cliquepatterns according to a number of points in the respective cliquepatterns.
 10. The method of claim 5, wherein determining the complexityof the patterns of activity comprises weighting a number of occurrencesof the clique patterns according to a directionality of the respectiveclique patterns.
 11. The method of claim 1, wherein: determining thecomplexity of the patterns of activity comprises dividing activity inthe recurrent artificial neural network and responsive to input into therecurrent artificial neural network into a plurality of windows of time,and determining the complexity of the patterns of the activity withineach of the windows of time; and determining the timing of the activityhaving the distinguishable complexity comprises identifying a firstwindow of time having the distinguishable complexity.
 12. The method ofclaim 1, wherein identifying the decision moment comprises identifyingtimes when the activity has a relatively high complexity indicative ofordered activity within the recurrent artificial neural network.
 13. Themethod of claim 1, further comprising reading one or more outputs of therecurrent artificial neural network at the decision moment, wherein theoutputs are based on the patterns of activity indicative of orderedactivity within the recurrent artificial neural network.
 14. The methodof claim 13, wherein the outputs a collection of multi-valued,non-binary digits, wherein the values each represent a weight assignedto edges in a corresponding of the patterns of activity.
 15. The methodof claim 13, further comprising transmitting or storing at least some ofthe read outputs, wherein transmitting or storing at least some of theread outputs comprises: transmitting or storing outputs associated withpatterns of activity having a relatively higher complexity; anddiscarding or ignoring outputs associated with patterns of activityhaving a relatively lower complexity.
 16. The method of claim 1, whereinthe input is a continuous stream of data input into the recurrentartificial neural network.
 17. A system comprising one or more computersoperable to perform operations, the operations comprising: determining acomplexity of patterns of activity in the recurrent artificial neuralnetwork; determining a timing of patterns of activity having acomplexity that is distinguishable from complexity of other patterns ofactivity; and identifying a decision moment based on the timing of thepatterns that have the distinguishable complexity.
 18. The system ofclaim 17, wherein determining the complexity of patterns comprisesdetermining a number of patterns enclosing cavities.
 19. The system ofclaim 17, wherein the activity is responsive to input into the recurrentartificial neural network.
 20. The system of claim 17, whereindetermining the timing of the patterns having the distinguishablecomplexity comprises determining the timing of patterns havingrelatively high complexity indicative of ordered activity within therecurrent artificial neural network, wherein the relatively highcomplexity is determined relative to a baseline level of patterncomplexity or to an average level of pattern complexity responsive to aplurality of different inputs.
 21. The system of claim 17, whereindetermining the complexity of the patterns of activity comprisesidentifying pre-defined clique patterns of activity of the recurrentartificial neural network.
 22. The system of claim 21, wherein themethod further comprises defining a plurality of windows of time duringwhich the activity of the recurrent artificial neural network isresponsive to an input into the recurrent artificial neural network,wherein the pre-defined clique patterns of activity are identified ineach of the pluralities of windows of time.
 23. The system of claim 22,wherein the method further comprises identifying a first window of timewithin the plurality of windows of time based on a distinguishablelikelihood of the pre-defined clique patterns of activity beingidentified during the first window.
 24. The system of claim 21, whereinidentifying clique patterns comprises identifying groups of directedcliques that enclose cavities.
 25. The system of claim 21, whereindetermining the complexity of the patterns of activity comprisesweighting a number of occurrences of the clique patterns according to anumber of points in the respective clique patterns.
 26. The system ofclaim 21, wherein determining the complexity of the patterns of activitycomprises weighting a number of occurrences of the clique patternsaccording to a directionality of the respective clique patterns.
 27. Thesystem of claim 17, wherein: determining the complexity of the patternsof activity comprises dividing activity in the recurrent artificialneural network and responsive to input into the recurrent artificialneural network into a plurality of windows of time, and determining thecomplexity of the patterns of the activity within each of the windows oftime; and determining the timing of the patterns of activity having thedistinguishable complexity comprises identifying a first window of timeduring which the patterns have the distinguishable complexity.
 28. Thesystem of claim 17, wherein identifying the decision moment comprisesidentifying times when the patterns of activity have encompass cavities.29. The system of claim 17, further comprising reading one or moreoutputs of the recurrent artificial neural network at the decisionmoment, wherein the outputs are based on the patterns of activityindicative of ordered activity within the recurrent artificial neuralnetwork.
 30. The system of claim 29, wherein the outputs comprise acollection of multi-valued, non-binary digits, wherein the values eachrepresent a weight assigned to edges in a corresponding of the patternsof activity.
 31. The system of claim 29, further comprising transmittingor storing at least some of the read outputs, wherein transmitting orstoring at least some of the read outputs comprises: transmitting orstoring outputs associated with patterns of activity having a relativelyhigher complexity; and discarding or ignoring outputs associated withpatterns of activity having a relatively lower complexity.
 32. Thesystem of claim 17, wherein the operations further comprise estimatingwhether the activity is responsive to the input into the recurrentartificial neural network, the estimating comprising: estimating thatrelatively simpler patterns of activity relatively soon after the inputevent are responsive the input but that relatively more complex patternsof activity relatively soon after the input event are not responsive theinput; and estimating that relatively more complex patterns of activityrelatively later after the input event are responsive the input but thatrelatively simpler patterns of activity relatively later after the inputevent are not responsive the input.